Update app.py

app.py

@@ -20,30 +20,26 @@ st.markdown('You can click "Browse files" multiple times until adding all images
 img_height = 224
 img_width = 224
 class_names = ['Palm', 'Others']
+model = tf.keras.models.load_model('model')
 
-uploaded_file = st.file_uploader("Upload image files", type="jpg", accept_multiple_files=True)
-st.image(uploaded_file, width=100)
+with st.form("my-form", clear_on_submit=True):
+        uploaded_file = st.file_uploader("Upload image files", type="jpg", accept_multiple_files=True)
+        submitted = st.form_submit_button("UPLOAD!")
 
-model = tf.keras.models.load_model('model')
-Generate_pred = st.button("Generate Prediction")
+    if submitted and file is not None:
+        st.write("UPLOADED!")
+        st.image(uploaded_file, width=100)
 
-if uploaded_file is not None:
-        
-    if Generate_pred:
-        for file in uploaded_file:
-            img = Image.open(file)
-            img_array = img_to_array(img)
-            img_array = tf.expand_dims(img_array, axis = 0) # Create a batch
-            processed_image = preprocess_input(img_array)
-                
-            predictions = model.predict(processed_image)
-            score = predictions[0]
-            st.markdown("Predicted class of the image {} is : {}".format(file, class_names[np.argmax(score)]))
-
-with st.form("list", clear_on_submit=True):
-    submitted = st.form_submit_button("clear")
-    if submitted:
-        uploaded_file = None
+        Generate_pred = st.button("Generate Prediction")
         
-        
-    
+        if Generate_pred:
+            for file in uploaded_file:
+                img = Image.open(file)
+                img_array = img_to_array(img)
+                img_array = tf.expand_dims(img_array, axis = 0) # Create a batch
+                processed_image = preprocess_input(img_array)
+                    
+                predictions = model.predict(processed_image)
+                score = predictions[0]
+                st.markdown("Predicted class of the image {} is : {}".format(file, class_names[np.argmax(score)]))
+